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Semantic Interoperability of Long-Tail Geoscience Resources over the Web
Published in Ashok N. Srivastava, Ramakrishna Nemani, Karsten Steinhaeuser, Large-Scale Machine Learning in the Earth Sciences, 2017
Mostafa M. Elag, Praveen Kumar, Luigi Marini, Scott D. Peckham, Rui Liu
Semantic heterogeneity occurs when there is a disagreement about the meaning of the label used to describe a resource or one of its parts, ambiguity about the interpretation of the content of a resource, or its usage pattern among related parties. Among the scientific communities, four sources of semantic heterogeneity can be identified: (1) Structural—that is, the syntax of the language used to describe and code the resources, including the natural language, programming languages, and data encoding languages; (2) Content—that is, the method and vocabularies used to describe the elements within each resource, including the naming process and missing information inside a file; (3) Conceptual—that is, the heterogeneity between related domains in the conceptualization of their information system, including heterogeneity in the structure of concepts, their level of granularity and relationships, and the terminologies that are used to describe these concepts; and (4) Contextual—that is, the mismatch between schemas used to map resources between information systems [38].
Hydrologie Metadata
Published in Praveen Kumar, Jay Alameda, Peter Bajcsy, Mike Folk, Momcilo Markus, Hydroinformatics: Data Integrative Approaches in Computation, Analysis, and Modeling, 2005
All of these collections exist in ASCII format and are easily accessible for download. These ASCII formats however are too simple in their representation and there is no formal implementation of any of the glossaries that could be used by application to parse and deduce meaning from these terms via the Internet. Because of the need to (1) keep the CV flexible, (2) update it easily, (3) maintain readability and access to the outside world, and (4) formalize it in a way that facilitates interoperability, the hydrologic CV is being developed and implemented in the web ontology language (OWL). Because an ontology is essentially a hierarchy of objects and their properties, it is an ideal application for categorizing a glossary of terms that can be divided along thematic areas going from more general to more specific. The use of OWL carries the prospect of providing a technology platform that can be used to overcome and resolve the problem of semantic heterogeneity in future.
Big Geospatial Data and the Geospatial Semantic Web: Current State and Future Opportunities
Published in Yulei Wu, Fei Hu, Geyong Min, Albert Y. Zomaya, Big Data and Computational Intelligence in Networking, 2017
Chuanrong Zhang, Weidong Li, Tian Zhao
Semantic heterogeneity refers to disagreements about meaning, interpretation, or intended use of the same or related data. By associating spatial data content from the Web with ontologies that would supply context and meaning, the vision of GSW is to extract geospatial knowledge from the Web regardless of geospatial data formats or sources; thus it can facilitate transparent geospatial data exchange, sharing, and query. For example, the GSW will be able to search for needed geospatial information not based on keywords, but on data contents and context, e.g., understanding what the word “near” means based on different spatial data contents and context.
Architecture and knowledge modelling for self-organized reconfiguration management of cyber-physical production systems
Published in International Journal of Computer Integrated Manufacturing, 2022
Timo Müller, Simon Kamm, Andreas Löcklin, Dustin White, Marius Mellinger, Nasser Jazdi, Michael Weyrich
In order to enable the realization of diverse methodologies within CPPSs, or CPSs in general, a knowledge modelling and management concept is a mandatory prerequisite. This concept cannot be considered completely detached from the systems architecture, as it has to be incorporated within the architectural approach. This means that the necessary knowledge must be held and provided to the system’s distributed components in such a way that they can implement their respective functionality based on it. Here, the basis for vertical as well as horizontal integration across all layers and components of a production system is a uniform information model (Hoffmann et al. 2013; Schmied et al. 2020). This is required since CPPS (or CPS in general) consist of components that originate from diverse vendors, leading to semantic heterogeneity (Constantin et al. 2020). The term semantic heterogeneity denotes the existence of diverse ways to express different/equivalent concepts (Jirkovský et al. 2016). Another driver of heterogeneity is the previously mentioned increasing frequency of changes to production systems. Besides semantic heterogeneity, especially syntactic and structural heterogeneities have to be considered in this regard (Kamm, Jazdi, and Weyrich 2021).
Industry 4.0: survey from a system integration perspective
Published in International Journal of Computer Integrated Manufacturing, 2020
Manuel Sanchez, Ernesto Exposito, Jose Aguilar
Moreover, on this layer, the semantic heterogeneity is solved, and RDF triples are created in order to populate the data with semantic information. The Data storage layer is in charge of storing the triples in the RDF format. Finally, the Analytic layer provides direct access to the storage layer for analysis tasks or customizing the user queries. The authors affirm that the main advantages of the integration using the SHS ontology is that the ontology describes the reality in its representation, and data can be easily queried in SPARQL. Same as previous works, this research is focused on put data available to other actors and dealing with the data heterogeneity issues. They allow the connection between actors. For that reason, this paper classifies it as belonging to the connection level.